Consumer AI is rapidly evolving beyond one-shot predictions toward systems that reason, plan, and adapt in real time. In this talk, Sudeep explores how personalization at DoorDash is shifting from static recommendation models to agentic, goal-driven AI experiences that operate across search, discovery, decision-making, and fulfillment. Using real examples inspired by DoorDash’s marketplace, and consumer surfaces, the session dives into how large language models, deep learning, and reinforcement-style feedback loops can be combined to build consumer-facing agents that understand intent, maintain context across sessions, and take coordinated actions—from helping users find the right meal to optimizing trade-offs across price, speed, quality, and reliability. Topics include agentic product discovery in a dense marketplace, multi-step decision flows that span browsing to checkout, building trust and controllability into consumer agents, and practical evaluation strategies for agentic systems operating at DoorDash scale. Attendees will leave with concrete frameworks for designing AI systems that feel helpful, adaptive, and human-centered—while remaining reliable, efficient, and grounded in real-world marketplace and business constraints.
Speaker
Sudeep Das
Head of Machine Learning and Artificial Intelligence, New Business Verticals @DoorDash, Previously Machine Learning Lead @Netflix, 15+ Years in Machine Learning
Sudeep Das is a senior machine learning and artificial intelligence leader with over 15 years of experience building large-scale, consumer-facing AI systems. He currently serves as Head of Machine Learning & AI for New Business Verticals at DoorDash, where he leads personalization, search, catalog intelligence, and decision-making systems across rapidly expanding consumer experiences including grocery, convenience, alcohol, and retail. At DoorDash, Sudeep focuses on applying deep learning, recommender systems, and generative AI to create highly adaptive, real-time consumer experiences. His work spans ranking and retrieval, large language model–powered discovery, and agentic systems that reason across user intent, context, and constraints to deliver personalized outcomes rather than static recommendations. Previously, Sudeep was a Machine Learning Lead at Netflix, where he helped develop next-generation personalization and discovery algorithms used by hundreds of millions of users worldwide. He holds a Ph.D. in Astrophysics from Princeton University and brings a strong scientific foundation to practical AI leadership at scale. Sudeep is a frequent speaker at leading international conferences including RecSys, SIGIR, ICML, and QCon, where he shares insights on production ML systems, personalization, and the future of intelligent consumer platforms.
